data normalization, model coefficient regularization, feeding the linear model to another downstream model), this is often not the fastest or cleanest method when a data analyst needs just a quick and easy way to determine the regression coefficients (and some basic associated statistics).
While this can offer additional advantages of applying other pipeline features of machine learning (e.g.
However, is there only one way to perform linear regression analysis in Python? In case of multiple available options, how to choose the most effective method?īecause of the wide popularity of the machine learning library scikit-learn, a common approach is often to call the Linear Model class from that library and fit the data. Therefore, it is critical for a data scientist to be aware of all the various methods he/she can quickly fit a linear model to a fairly large data set and asses the relative importance of each feature in the outcome of the process. On the other hand, Python is fast emerging as the de-facto programming language of choice for data scientists. Thus, a large body of natural phenomena can be modeled (approximately) using these transformations and linear model even if the functional relationship between the output and features are highly nonlinear. As pointed out in this article, ‘ LINEAR’ term in the linear regression model refers to the coefficients, and not to the degree of the features.įeatures (or independent variables) can be of any degree or even transcendental functions like exponential, logarithmic, sinusoidal. The importance of fitting (accurately and quickly) a linear model to a large data set cannot be overstated. We gloss over their pros and cons, and show their relative computational complexity measure.įor many data scientists, linear regression is the starting point of many statistical modeling and predictive analysis projects. In this article, we discuss 8 ways to perform simple linear regression using Python code/packages.